import psycopg2

import pandas as pd

# Make our connection/cursor function

def get_conn_cur(): # define function name and arguments (there aren't any)

# Make a connection

conn = psycopg2.connect(

host="ticketsdbinstance.cnt7cm8tgir1.us-west-1.rds.amazonaws.com",

database="sales_db",

user="postgres",

password="ISTA322SALESDB",

port='5432')

cur = conn.cursor() # Make a cursor after

return(conn, cur) # Return both the connection and the cursor

# Same run_query function

def run_query(query_string):

conn, cur = get_conn_cur() # get connection and cursor

cur.execute(query_string) # executing string as before

my_data = cur.fetchall() # fetch query data as before

# here we're extracting the 0th element for each item in cur.description

colnames = [desc[0] for desc in cur.description]

cur.close() # close

conn.close() # close

return(colnames, my_data) # return column names AND data

# Column name function for checking out what's in a table

def get_column_names(table_name): # arguement of table_name

conn, cur = get_conn_cur() # get connection and cursor

# Now select column names while inserting the table name into the WERE

column_name_query = """SELECT column_name FROM information_schema.columns

WHERE table_name = '%s' """ %table_name

cur.execute(column_name_query) # exectue

my_data = cur.fetchall() # store

cur.close() # close

conn.close() # close

return(my_data) # return

# Check table_names

def get_table_names():

conn, cur = get_conn_cur() # get connection and cursor

# query to get table names

table_name_query = """SELECT table_name FROM information_schema.tables

WHERE table_schema = 'public' """

cur.execute(table_name_query) # execute

my_data = cur.fetchall() # fetch results

cur.close() #close cursor

conn.close() # close connection

return(my_data) # return your fetched results

# make sql_head function

def sql_head(table_name):

conn, cur = get_conn_cur() # get connection and cursor

# Now select column names while inserting the table name into the WERE

head_query = """SELECT * FROM %s LIMIT 5; """ %table_name

cur.execute(head_query) # exectue

colnames = [desc[0] for desc in cur.description] # get column names

my_data = cur.fetchall() # store first five rows

cur.close() # close

conn.close() # close

df = pd.DataFrame(data = my_data, columns = colnames) # make into df

return(df) # return

In the first section there's a small dataset that you can import on the incomes of people that are either in school, working, or retired. Write a groupby statement that returns the average income of each group as well as the min and max incomes within the group. Python code please ASAP!